# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python2, python3
"""Optimize the logP of a molecule."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import functools
import json
import os
from absl import app
from absl import flags
from rdkit import Chem

from dqn import deep_q_networks
from dqn import molecules as molecules_mdp
from dqn import run_dqn
from dqn.py import molecules
from dqn.tensorflow_core import core

flags.DEFINE_float("gamma", 0.999, "discount")
FLAGS = flags.FLAGS


class Molecule(molecules_mdp.Molecule):

    def _reward(self):
        molecule = Chem.MolFromSmiles(self._state)
        if molecule is None:
            return 0.0
        return molecules.penalized_logp(molecule)


def main(argv):
    del argv
    if FLAGS.hparams is not None:
        with open(FLAGS.hparams, "r") as f:
            hparams = deep_q_networks.get_hparams(**json.load(f))
    else:
        hparams = deep_q_networks.get_hparams()

    environment = Molecule(
        atom_types=set(hparams.atom_types),
        init_mol=FLAGS.start_molecule,
        allow_removal=hparams.allow_removal,
        allow_no_modification=hparams.allow_no_modification,
        allow_bonds_between_rings=hparams.allow_bonds_between_rings,
        allowed_ring_sizes=set(hparams.allowed_ring_sizes),
        max_steps=hparams.max_steps_per_episode,
    )

    dqn = deep_q_networks.DeepQNetwork(
        input_shape=(hparams.batch_size, hparams.fingerprint_length + 1),
        q_fn=functools.partial(deep_q_networks.multi_layer_model,
                               hparams=hparams),
        optimizer=hparams.optimizer,
        grad_clipping=hparams.grad_clipping,
        num_bootstrap_heads=hparams.num_bootstrap_heads,
        gamma=hparams.gamma,
        epsilon=1.0,
    )

    run_dqn.run_training(
        hparams=hparams,
        environment=environment,
        dqn=dqn,
    )

    core.write_hparams(hparams, os.path.join(FLAGS.model_dir, "config.json"))


if __name__ == "__main__":
    app.run(main)
